Failure Recovery with Shared Autonomy

2012

Conference Paper

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Building robots capable of long term autonomy has been a long standing goal of robotics research. Such systems must be capable of performing certain tasks with a high degree of robustness and repeatability. In the context of personal robotics, these tasks could range anywhere from retrieving
items from a refrigerator, loading a dishwasher, to setting up a dinner table. Given the complexity of tasks there are a multitude of failure scenarios that the robot can encounter, irrespective
of whether the environment is static or dynamic. For a robot to be successful in such situations, it would need to know how to recover from failures or when to ask a human for help.
This paper, presents a novel shared autonomy behavioral executive to addresses these issues. We demonstrate how this executive combines generalized logic based recovery and human
intervention to achieve continuous failure free operation. We tested the systems over 250 trials of two different use case experiments. Our current algorithm drastically reduced human intervention from 26% to 4% on the first experiment and 46% to 9% on the second experiment. This system provides a new dimension to robot autonomy, where robots can exhibit long term failure free operation with minimal human supervision. We also discuss how the system can be generalized.

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Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems